Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-12-03T19:19:35.200Z Has data issue: false hasContentIssue false
  • English
  • Français

Inference of Automata by dialectic learning

Published online by Cambridge University Press:  09 March 2009

M. Richetin  and
M. Naranjo
M. Richetin
Affiliation:
Electronics Laboratory, University of Clermont II, BP 45, 63170 Aubiere (France)
M. Naranjo
Affiliation:
Electronics Laboratory, University of Clermont II, BP 45, 63170 Aubiere (France)
Get access Rights & Permissions [Opens in a new window]

Summary

An algorithm for the inference of the external behaviour model of an automaton is given. It uses a sequential learning procedure based on induction-contradiction-correction concepts. The induction is a generalization of relationships between automaton state properties, and the correction consists in a more and more accurate discrimination of the automaton state properties. These properties are defined from the input/output contradictory sequences which are discovered after the observed contradictions between successive predictions and observations.

Type
Article
Information
Robotica , Volume 3 , Issue 3 , September 1985 , pp. 159 - 163
Copyright
Copyright © Cambridge University Press 1985

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Barr, A. and Feigenbaum, E.A., The Handbook of Artificial Intelligence Vol. 1 (Pitman, London, 1981).Google Scholar
2.Bundy, A. and Silver, B., “A Critical Survey of Rule Learning Programs” Proceedings of the 1982 European Conference on Artificial Intelligence (Orsay, July, 1982) pp. 151–157.Google Scholar
3.Show, T.S., “Testing software design modelled by finitestate machines” IEEE Trans. on Software Engineering. SE.4, No. 3, 176–187 (May, 1978).Google Scholar
4.Koenig, E.C., “Associating current knowledge with that of past experience based on knowledge about automata” Kybernetes 11, No. 3, 211–217 (1982).Google Scholar
5.Biermann, A.W., “On the inference of Turing machine from sample computations” Artificial Intelligence 3, 181–198 (1972).CrossRefGoogle Scholar
6.Fu, K.S., Syntactic Pattern Recognition (Prentice Hall, Englewood Cliffs, NJ, 1982).Google Scholar
7.Veelenturf, L.P.J., “An automata theoretical approach to developing learning neural networks” Cybernetics and Systems 12, 179–202 (1981).Google Scholar
8.Dufay, B. and Latombe, J.C., “Robot programming by inductive inference” 8th IJCAI, Karlsruhe (1983).Google Scholar
9.Biermann, A.W. and Feldman, J.A., “On the synthesis of finite-state machines from samples of their behaviour”, IEEE Trans. on Computers C-21, No. 6, 592–597 (June, 1972).Google Scholar
10.Kohavi, Z., Switching and Finite Automata Theory (McGraw-Hill, New York, 1970).Google Scholar
11.Luneau, P., Richetin, M. and Cayla, C., “Sequential Learning of Automata from Input/Output Behaviour” Robotica 1, 151–159 (1984).Google Scholar
12.Aho, A.V. and Uliman, J.D., The Theory of Parsing, Translation and Compiling (Prentice-Hall, Englewood Cliffs, N.J., 1972).Google Scholar
13.Naranjo, M., Doctoral Thesis: Control and Identification of Automata from Stochastic Information University of Clermont II. Electronic L.aboratory, University of Clermont II (January, 1984).Google Scholar